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            <div class="post-toc animated"><ol class="nav"><li class="nav-item nav-level-1"><a class="nav-link" href="#1-%E5%89%8D%E8%A8%80"><span class="nav-text">1 前言</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#1-1-%E4%B8%BA%E4%BB%80%E4%B9%88%E8%A6%81%E9%9B%86%E6%88%90Hive%E5%92%8CHBase"><span class="nav-text">1.1 为什么要集成Hive和HBase?</span></a></li></ol></li><li class="nav-item nav-level-1"><a class="nav-link" href="#2-Hive%E9%9B%86%E6%88%90HBase%E7%9A%84%E5%8E%9F%E7%90%86"><span class="nav-text">2 Hive集成HBase的原理</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#3-%E4%BD%BF%E7%94%A8%E5%9C%BA%E6%99%AF"><span class="nav-text">3 使用场景</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#4-%E6%95%B4%E5%90%88"><span class="nav-text">4 整合</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#4-1-%E7%A4%BA%E4%BE%8B"><span class="nav-text">4.1 示例</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#4-2-%E5%AD%97%E6%AE%B5%E6%98%A0%E5%B0%84"><span class="nav-text">4.2 字段映射</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#4-3-%E6%95%B0%E6%8D%AE%E5%90%8C%E6%AD%A5%E6%B5%8B%E8%AF%95"><span class="nav-text">4.3 数据同步测试</span></a></li></ol></li><li class="nav-item nav-level-1"><a class="nav-link" href="#%E5%8F%82%E8%80%83%E8%B5%84%E6%BA%90"><span class="nav-text">参考资源</span></a></li></ol></div>
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          Hive集成HBase 详解
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<h1 id="1-前言"><a href="#1-前言" class="headerlink" title="1 前言"></a>1 前言</h1><h2 id="1-1-为什么要集成Hive和HBase"><a href="#1-1-为什么要集成Hive和HBase" class="headerlink" title="1.1 为什么要集成Hive和HBase?"></a>1.1 为什么要集成Hive和HBase?</h2><p>Kylin的三大依赖模块分别是数据源、构建引擎和存储引擎。默认这三者分别是Hive、MapReduce和HBase。但随着调研和使用的深入，渐渐有用户发现它们均存在不足之处。</p>
<p>比如 Hive 不应该用来进行实时的查询，因为它需要很长时间才可以返回结果。同时想要将数据实时的插入hive中则完全没有可操作性。</p>
<p>至于 HBase虽然非常适用于海量明细数据（十亿、百亿）的随机实时查询但只提供了简单的基于 Key 值的快速查询能力—，没法进行大量的条件查询，对于数据分析来说，不太友好。</p>
<p>在大数据架构中，Hive和HBase是协作关系，Hive方便地提供了Hive QL的接口来简化MapReduce的使用， 而HBase提供了低延迟的数据库访问。如果两者结合，可以利用MapReduce的优势针对HBase存储的大量内容进行离线的计算和分析。</p>
<h1 id="2-Hive集成HBase的原理"><a href="#2-Hive集成HBase的原理" class="headerlink" title="2 Hive集成HBase的原理"></a>2 Hive集成HBase的原理</h1><p>Hive 整合 hbase 为用户提供一种 sqlOnHbase 的方法。Hive 与 HBase 整合的实现是利用两者本身对外的 API 接口互相通信来完成的，其具体工作交由 Hive 的 lib 目录中的 <code>hive-hbase-handler-xxx.jar</code> 工具类来实现对 HBase 数据的读取。</p>
<p>通过HBaseStorageHandler，Hive可以获取到Hive表所对应的HBase表名，列簇和列，InputFormat、OutputFormat类，创建和删除HBase表等。</p>
<p>Hive访问HBase中HTable的数据，实质上是通过MR读取HBase的数据，而MR是使用HiveHBaseTableInputFormat完成对表的切分，获取RecordReader对象来读取数据的。</p>
<p>对HBase表的切分原则是一个Region切分成一个Split,即表中有多少个Regions,MR中就有多少个Map；</p>
<p>读取HBase表数据都是通过构建Scanner，对表进行全表扫描，如果有过滤条件，则转化为Filter。当过滤条件为rowkey时，则转化为对rowkey的过滤；Scanner通过RPC调用RegionServer的next()来获取数据。</p>
<h1 id="3-使用场景"><a href="#3-使用场景" class="headerlink" title="3 使用场景"></a>3 使用场景</h1><h1 id="4-整合"><a href="#4-整合" class="headerlink" title="4 整合"></a>4 整合</h1><p>因为Hive与HBase集成是利用两者本身对外的API接口互相通信来完成的，其具体工作交由Hive的lib目录中的hive-hbase-handler-.jar工具类来实现。所以只需要将hive的 <code>hive-hbase-handler-.jar</code> 复制到<code>hbase/lib</code>中就可以了。</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">cp &#x2F;usr&#x2F;local&#x2F;hive&#x2F;lib&#x2F;hive-hbase-handler-2.3.7.jar &#x2F;usr&#x2F;local&#x2F;hbase&#x2F;lib&#x2F;</span><br></pre></td></tr></table></figure>

<p><strong>注</strong>: 如果在hive整合hbase中，出现版本之类的问题，那么以hbase的版本为主，将hbase中的jar包覆盖hive的jar包。</p>
<h2 id="4-1-示例"><a href="#4-1-示例" class="headerlink" title="4.1 示例"></a>4.1 示例</h2><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br></pre></td><td class="code"><pre><span class="line"># Hive 语句</span><br><span class="line"><span class="keyword">create</span> <span class="keyword">table</span> t_employee(id <span class="type">int</span>,name string) </span><br><span class="line">stored <span class="keyword">by</span> <span class="string">&#x27;org.apache.hadoop.hive.hbase.HBaseStorageHandler&#x27;</span> </span><br><span class="line"><span class="keyword">with</span> serdeproperties(&quot;hbase.columns.mapping&quot;<span class="operator">=</span>&quot;:key,st1:name&quot;) </span><br><span class="line">tblproperties(&quot;hbase.table.name&quot;<span class="operator">=</span>&quot;t_employee&quot;,&quot;hbase.mapred.output.outputtable&quot; <span class="operator">=</span> &quot;t_employee&quot;);</span><br><span class="line"></span><br><span class="line"># hbase 语句</span><br><span class="line">list</span><br><span class="line"><span class="keyword">describe</span> <span class="string">&#x27;t_employee&#x27;</span></span><br><span class="line"># 返回结果</span><br><span class="line"><span class="keyword">Table</span> t_employee <span class="keyword">is</span> ENABLED</span><br><span class="line">t_employee</span><br><span class="line"><span class="keyword">COLUMN</span> FAMILIES DESCRIPTION</span><br><span class="line">&#123;NAME =&gt; &#x27;st1&#x27;, BLOOMFILTER =&gt; &#x27;ROW&#x27;, VERSIONS =&gt; &#x27;1&#x27;, IN_MEMORY =&gt; &#x27;false&#x27;, KEEP_DELETED_CELLS =&gt; &#x27;FALSE&#x27;, DATA_BLOCK_ENCODING =&gt; &#x27;NONE&#x27;, TTL =&gt; &#x27;FOREVER&#x27;, COMPRESSION =&gt; &#x27;NONE&#x27;</span><br><span class="line">, MIN_VERSIONS =&gt; &#x27;0&#x27;, BLOCKCACHE =&gt; &#x27;true&#x27;, BLOCKSIZE =&gt; &#x27;65536&#x27;, REPLICATION_SCOPE =&gt; &#x27;0&#x27;&#125;</span><br><span class="line"><span class="number">1</span> <span class="type">row</span>(s) <span class="keyword">in</span> <span class="number">0.0260</span> seconds</span><br></pre></td></tr></table></figure>

<p>说明：</p>
<ol>
<li><p>这里面出现了三个t_employee表名，第一个t_employee 是hive表中的名称，(id int,name string) 是hive表结构。</p>
<p>在tblproperties 语句中还出现了2个t_employee表，”hbase.table.name”定义的是在hbase中的表名 ，这个属性是可选的，仅当你想在Hive和Hbase中使用不同名字的表名时才需要填写，如果使用相同的名字则可以省略；</p>
<p>“hbase.mapred.output.outputtable”定义的第三个t_employee是存储数据表的名称，指定插入数据时写入的表，如果以后需要往该表插入数据就需要指定该值，这个可以不要，表数据就存储在第二个表中了 。</p>
</li>
<li><p>stored by ‘org.apache.hadoop.hive.hbase.HBaseStorageHandler’ ：是指定处理的存储器，就是hive-hbase-handler-*.jar包，要做hive和hbase的集成必须要加上这一句；</p>
</li>
<li><p>“hbase.columns.mapping” 是定义在hive表中的字段怎么与hbase的列族进行映射。</p>
<p>   例如:st1就是列族，name就是列。它们之间通过“：”连接。</p>
<p>   在hive中创建的t_employee表，包括两个字段（int型的id和string型的name），映射为hbase中的表t_employee，其中：key对应hbase的rowkey，value对应hbase的st1:name列。</p>
</li>
</ol>
<h2 id="4-2-字段映射"><a href="#4-2-字段映射" class="headerlink" title="4.2 字段映射"></a>4.2 字段映射</h2><p>控制HBase字段和Hive之间的映射有两种<code>SERDEPROPERTIES</code>:</p>
<ul>
<li>hbase.columns.mapping</li>
<li>hbase.table.default.storage.type，可以是string(default)或binary中的任一个，指定这个选项只有在Hive 0.9之后可使用.</li>
</ul>
<p>目前所支持的字段映射多少是有些难处理或存在约束的：</p>
<ul>
<li>对于每一个Hive字段，表的创建者必须用逗号分隔的字符串（<code>hbase.columns.mapping</code>）指定对应的入口（Hive表有n个字段，则该字符串得指定n个入口），在各个入口之间不能由空格（因为空格会被解析成字段名中的一部分）。</li>
<li>映射入口必须是以下两者之一：<strong>行健</strong>或**’列族名:[列名][#(binary|string)]’**<ul>
<li>如果没有指定类型，则直接使用<code>hbase.table.default.storage.type</code>的值</li>
<li>合法值的的前缀也是合法的（例如#b表示#binary）</li>
<li>如果指定某字段为binary，则对应的HBase中的单元格则应该是HBase的Bytes类的内容组成</li>
</ul>
</li>
<li>必须要有确切的行健映射</li>
<li>如果没有指定列名，则默认使用Hive的字段名作为HBase中的列名</li>
</ul>
<h2 id="4-3-数据同步测试"><a href="#4-3-数据同步测试" class="headerlink" title="4.3 数据同步测试"></a>4.3 数据同步测试</h2><p>进入hbase之后，在t_employee中添加两条数据 然后查询该表</p>
<figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">put <span class="string">&#x27;t_employee&#x27;</span>,<span class="string">&#x27;1001&#x27;</span>,<span class="string">&#x27;st1:name&#x27;</span>,<span class="string">&#x27;zhaoqian&#x27;</span></span><br><span class="line">put <span class="string">&#x27;t_employee&#x27;</span>,<span class="string">&#x27;1002&#x27;</span>,<span class="string">&#x27;st1:name&#x27;</span>,<span class="string">&#x27;sunli&#x27;</span></span><br><span class="line">scan <span class="string">&#x27;t_employee&#x27;</span></span><br></pre></td></tr></table></figure>

<p>查询结果：</p>
<p><img src="https://gitee.com/littlefxc/oss/raw/master/images/hive_on_hbase.png" alt="Hive%E9%9B%86%E6%88%90HBase%20%E8%AF%A6%E8%A7%A3%20100037f2aa264a2c835a0530a373a912/Untitled.png"></p>
<p>然后切换到hive中查询该表</p>
<p><img src="https://gitee.com/littlefxc/oss/raw/master/images/hive_on_hbase2.png" alt="Hive%E9%9B%86%E6%88%90HBase%20%E8%AF%A6%E8%A7%A3%20100037f2aa264a2c835a0530a373a912/Untitled%201.png"></p>
<p>至此，hive 集成hbase 就到此告一段落。</p>
<h1 id="参考资源"><a href="#参考资源" class="headerlink" title="参考资源"></a>参考资源</h1><p><a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/74041611">如何整合hive和hbase</a></p>
<p><a target="_blank" rel="noopener" href="https://ask.hellobi.com/blog/marsj/4002">Hive与HBase的集成实践</a></p>
<p>[[hbase-1-4-13-安装部署]]</p>
<p>[[hive2-3-7安装]]</p>

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